Scale-invariant feature transform (SIFT) [1]}, [2]}, [3]} is one of the most classic, effective, and commonly used feature point matching methods. It first constructs a Gaussian scale space, supported by scale-space theory [4]}, and extracts local extreme points in a DoG (Difference of Gaussian) pyramid as the feature points. Then, feature points are described by statistical gradient histograms. SIFT has a good scale and rotation invariance. Many improved algorithms based on SIFT were proposed in recent years. [5]}, [6]}, [7]}, [8]}, [9]}, [10]}, [11]}
Harris et al [12]} proposed a feature point detection method, which detects the corner structure that changes sharply in multiple directions in an image by calculating corner response according to the gradient. It is an effective and stable feature points detection algorithm, which is widely applied in feature-based image registration.
The oriented FAST and rotated BRIEF (ORB) [13]} uses improved accelerated segment test (FAST) [14]} and directional binary robust independent elementary features (BRIEF) [15]} respectively for feature points detection and description. The algorithm has low time complexity, which is suitable for real-time registration.
Binary robust invariant scalable keypoints (BRISK) [16]} and fast retina keypoint (FREAK) [17]} are the improved algorithms of BRIEF, which are scale invariant, rotation invariant, and robust to noise. KAZE feature[18]} uses the nonlinear diffusion filtering method to establish the scale-space without blurring the image, different from SIFT.
